EP4020805A1 - Appareil de traitement de signal, appareil d'imagerie par résonance magnétique, et procédé de traitement de signal - Google Patents

Appareil de traitement de signal, appareil d'imagerie par résonance magnétique, et procédé de traitement de signal Download PDF

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Publication number
EP4020805A1
EP4020805A1 EP21216525.2A EP21216525A EP4020805A1 EP 4020805 A1 EP4020805 A1 EP 4020805A1 EP 21216525 A EP21216525 A EP 21216525A EP 4020805 A1 EP4020805 A1 EP 4020805A1
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Prior art keywords
filter
integral value
sequence
coefficient
signal processing
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EP21216525.2A
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German (de)
English (en)
Inventor
Hidenori Takeshima
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Canon Medical Systems Corp
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Canon Medical Systems Corp
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Priority claimed from JP2021198574A external-priority patent/JP2022103084A/ja
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Publication of EP4020805A1 publication Critical patent/EP4020805A1/fr
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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/06—Non-recursive filters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/543—Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/565—Correction of image distortions, e.g. due to magnetic field inhomogeneities
    • G01R33/56518—Correction of image distortions, e.g. due to magnetic field inhomogeneities due to eddy currents, e.g. caused by switching of the gradient magnetic field
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0223—Computation saving measures; Accelerating measures
    • H03H17/0227—Measures concerning the coefficients

Definitions

  • Embodiments described herein relate generally to a signal processing apparatus, a magnetic resonance imaging apparatus, and a signal processing method.
  • a signal processing apparatus configured to compute a first integral value corresponding to an element in a coefficient sequence of a first input sequence and a second integral value corresponding to the element in a coefficient sequence of a second input sequence next to the first input sequence
  • the signal processing apparatus includes an integral value computing unit configured to add a value not overlapping the first input sequence in the second input sequence to the first integral value and subtract a value not overlapping the second input sequence in the first input sequence from the first integral value for the element, thereby computing the second integral value.
  • the signal processing apparatus may further include a signal value computing unit configured to compute an output signal value corresponding to the second input sequence based on the second integral value and the coefficient sequence of the second input sequence.
  • the signal processing apparatus may be configured to compute a third integral value corresponding to the element in a coefficient sequence of a third input sequence next to the second input sequence, wherein the integral value computing unit may be configured to add a value not overlapping the second input sequence in the third input sequence to the second integral value and subtract a value not overlapping the third input sequence in the second input sequence from the second integral value for the element, thereby computing the third integral value, and the signal value computing unit may be configured to compute an output signal value corresponding to the third input sequence based on the third integral value and the coefficient sequence of the third input sequence.
  • the coefficient sequence may correspond to a sequence of a series of amplification factors corresponding to an input sequence.
  • the integral value computing unit and the signal value computing unit may constitute a finite impulse response digital filter.
  • the element may be a representative coefficient representing a plurality of filter coefficients included in a range defined by values of the filter coefficients, and the coefficient sequence may be a series of the representative coefficients.
  • the number of bits in the filter coefficients, the representative coefficient, the first integral value, and the second integral value may be larger than number of bits of the first input sequence and the second input sequence.
  • the number of bits in the filter coefficients, the representative coefficient, the first integral value, and the second integral value may be larger than 32.
  • the range may be set by comparing an arithmetic result using the filter coefficients with a threshold or comparing the filter coefficients with a threshold.
  • a magnetic resonance imaging apparatus includes: the signal processing apparatus, wherein the signal value computing unit may be configured to use a signal value for controlling an electric current to be supplied to a gradient coil in a pulse sequence as the input sequence, and may be configured to output a position in a k-space as the output signal value, and the magnetic resonance imaging apparatus further includes an adjusting unit may be configured to correct a position in the k-space of magnetic resonance data generated by performing the pulse sequence or modify the pulse sequence based on the position in the k-space.
  • the non-overlapping may correspond to delay in the input sequence.
  • the signal processing apparatus may further include a storage unit configured to store therein a partial filter coefficient sequence composed of a plurality of filter coefficients arrayed in order of filter indices for distinguishing the respective filter coefficients belonging to each of a plurality of ranges of the amplification factor for the input signal input to the finite impulse response filter in units of a set index for distinguishing a set of the filter indices; a range determining unit configured to determine the range of the amplification factor of the filter coefficients determined to be included in the set of the filter indices based on the partial filter coefficient sequence corresponding to the set of the filter indices; and a representative coefficient determining unit configured to determine the representative coefficient representing the filter coefficients for each of the ranges of the amplification factor.
  • the representative coefficient determining unit may be configured to compute an average of the filter coefficients for each of the ranges of the amplification factor, thereby determining the representative coefficient.
  • the number of bits in the filter coefficients and the representative coefficient may be larger than 32.
  • the signal processing apparatus may compute the second integral value using the first integral value without re-computing the first integral value.
  • a signal processing method includes: computing a first integral value corresponding to an element in a coefficient sequence of a first input sequence; adding a value not overlapping the first input sequence in a second input sequence, which is next to the first input sequence, to the first integral value and subtracting a value not overlapping the second input sequence in the first input sequence from the first integral value for the element, thereby computing a second integral value corresponding to the element in a coefficient sequence of the second input sequence.
  • a signal processing apparatus is configured to compute a first integral value corresponding to an element in a coefficient sequence of a first input sequence and a second integral value corresponding to the element in a coefficient sequence of a second input sequence next to the first input sequence, in which the element is a representative coefficient representing a plurality of filter coefficients included in a range defined by values of the filter coefficients, and the coefficient sequence is a series of the representative coefficients, the signal processing apparatus includes an integral value computing unit configured to compute the second integral value for the element based on the first integral value and the second input sequence.
  • a signal processing apparatus for an magnetic resonance imaging apparatus is configured to receive an input which is a signal value for controlling an electric current to be supplied to a gradient coil in a pulse sequence as the input sequence, and output a position in a k-space as the output signal value, the signal processing apparatus being configured to compute a first integral value from the received input, the first integral value corresponding to an element in a coefficient sequence of a first input sequence and a second integral value corresponding to the element in a coefficient sequence of a second input sequence next to the first input sequence, the signal processing apparatus being configured to derive a position in k-space from the first integral value and a position in k-space for the second integral value, the signal processing apparatus comprise: an integral value computing unit configured to add a value not overlapping the first input sequence in the second input sequence to the first integral value and subtract a value not overlapping the second input sequence in the first input sequence from the first integral value for the element, thereby computing the second integral value.
  • FIG. 1 is a block diagram of an example of a signal processing apparatus 1.
  • Technical ideas according to the embodiments are not necessarily embodied by the configuration illustrated in FIG. 1 and may be embodied by hardware, such as electric circuitry including various circuitry elements.
  • the signal processing apparatus 1 includes a communication interface 11, a memory 13, and processing circuitry 15.
  • the signal processing apparatus 1 functions as a filter arithmetic apparatus including a digital filter that performs a finite impulse response (FIR) arithmetic operation on input signals (hereinafter, referred to as an FIR filter), for example.
  • FIR finite impulse response
  • FIG. 1 the communication interface 11, the memory 13, and the processing circuitry 15 are electrically coupled by a bus in the signal processing apparatus 1.
  • the signal processing apparatus 1 is coupled to a network via the communication interface 11.
  • the network illustrated in FIG. 1 is coupled to a filter parameter determining apparatus 3, a result output apparatus 5, and a signal input apparatus 7, for example.
  • the result output apparatus 5 is an apparatus to which processing results of the FIR filter in the signal processing apparatus 1 are output.
  • the signal input apparatus 7 is an apparatus that receives input signals to be input to the FIR filter in the signal processing apparatus 1.
  • the filter parameter determining apparatus 3 includes a memory 33 and processing circuitry 30.
  • the memory 33 is provided as storage circuitry that stores therein various kinds of information.
  • the memory 33 is a storage apparatus, such as a hard disk drive (HDD), a solid state drive (SSD), and an integrated circuit storage device.
  • the memory 33 is not limited to an HDD or an SSD and may be a semiconductor memory element, such as a random access memory (RAM) and a flash memory, an optical disc, such as a compact disc (CD) and a digital versatile disc (DVD), or a drive apparatus that reads and writes various kinds of information from and to a portable storage medium and a semiconductor memory element, such as a RAM.
  • the memory 33 in the filter parameter determining apparatus 3 stores therein partial filter coefficient sequences corresponding to respective filter index sets.
  • the following describes definition of a filter index, a filter index set, a total filter coefficient sequence, a partial filter coefficient sequence, and a plurality of filter index sets with reference to FIG. 2 and FIG. 3 .
  • FIG. 2 is a diagram for explaining definition of a filter index set, a total filter coefficient sequence, a partial filter coefficient sequence, and a plurality of filter index sets.
  • a filter index in the FIR filter is distinguished by an index (hereinafter, referred to as a filter index).
  • the filter index is an integer of 0 or larger indicating the number of stages for shifting input signals to be input to the FIR filter and corresponds to time and the position of an image, for example.
  • the filter index may be the number of stages of delay in input signals to be input to the FIR filter.
  • the horizontal axis is a time axis or a direction having properties similar to those of the time axis (hereinafter, referred to as a pseudo time-axis direction).
  • the vertical axis is a direction indicating the magnitude of the filter coefficient, that is, a direction indicating the amplification factor for the input signals (hereinafter, referred to as an amplification factor axis direction).
  • the filter coefficient is represented by a bar-like rectangle having the length corresponding to the amplification factor for the input signals and disposed at the position corresponding to the filter index.
  • the filter coefficient sequence is a sequence of filter coefficients associated with the respective filter indices.
  • the total filter coefficient sequence is a sequence of filter coefficients in order of the filter indices from 0 to (the degree of filters - 1).
  • the partial filter coefficient sequence is a partial filter coefficient sequence associated with consecutive filter indices in the total filter coefficient sequence.
  • the partial filter coefficient sequences are each composed of a series of filter coefficients in a dotted rectangle.
  • the filter index set is a set of a series of filter indices corresponding to the partial filter coefficient sequence.
  • the filter index set includes the filter indices of a plurality of filter coefficients in the partial filter coefficient sequence as elements. In FIG.
  • the filter index set is represented by a double-pointed arrow positioned under the partial filter coefficient sequence represented by a dotted rectangle.
  • the filter index set corresponds to an interval for extracting the partial filter coefficient sequence from the total filter coefficient sequence.
  • a plurality of filter index sets are each distinguished by an index for distinguishing the filter index set (hereinafter, referred to as a set index).
  • the filter index sets are associated with respective set indices.
  • the index corresponding to a set of filter index sets is a set index set.
  • the set index set includes the set indices as elements.
  • the memory 33 corresponds to a storage unit in the filter parameter determining apparatus 3.
  • the total of filter indices included in a plurality of filter index sets corresponds to a large number of taps, such as 1,000,000 or 3,000,000 taps.
  • the partial filter coefficient sequence stored in the memory 33 is part of the total filter coefficient sequence acquired by measurement using a filter coefficient acquiring apparatus, for example.
  • the partial filter coefficient sequence is part of a coefficient sequence generated by the filter coefficient acquiring apparatus using filter parameters supplied by a filter designer and a user, for example. Measurement of the filter coefficient and generation of the filter coefficient sequence using the filter coefficient acquiring apparatus are not explained herein because they can be performed using various known technologies.
  • the processing circuitry 30 in the filter parameter determining apparatus 3 collectively controls the filter parameter determining apparatus 3.
  • the processing circuitry 30 includes a range determination function 31 and a representative coefficient determination function 32 relating to determination of filter parameters used for computation in the FIR filter in the signal processing apparatus 1, for example.
  • the processing circuitry 30 that implements the range determination function 31 and the representative coefficient determination function 32 corresponds to a range determining unit and a representative coefficient determining unit.
  • the range determination function 31, the representative coefficient determination function 32, and other functions are stored in the memory 33 as computer-executable programs.
  • the processing circuitry 30 is a processor.
  • the processing circuitry 30, reads out a computer program from the memory 33 and executes it, thereby implementing the function corresponding to the computer program. In other words, the processing circuitry 30 that has read out the computer programs has the range determination function 31 and the representative coefficient determination function 32.
  • the filter parameters, the range determination function 31, and the representative coefficient determination function 32 will be described later.
  • processor reads out the computer programs corresponding to the respective functions from the memory and executes them in the description above, the embodiment is not limited thereto.
  • the term “processor” means circuitry, such as a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD)), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA).
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • SPLD simple programmable logic device
  • CPLD complex programmable logic device
  • FPGA field programmable gate array
  • the processor If the processor is a CPU, for example, the processor reads out a computer program stored in the memory and executes it, thereby implementing the corresponding function. If the processor is an ASIC, the computer program is not stored in the memory, and the function is directly incorporated in circuitry of the processor as a logic circuit.
  • the processors according to the present embodiment are not necessarily each provided as single circuitry. Alternatively, a plurality of independent circuitry may be combined to serve as one processor and implement the functions. While the computer programs corresponding to the range determination function 31 and the representative coefficient determination function 32 are stored in single storage circuitry in the description above, the embodiment is not limited thereto. A plurality of storage circuitry may be dispersed and disposed, and the processing circuitry may read out a computer program from the corresponding storage circuitry.
  • the processing circuitry 30 acquires partial filter coefficient sequences corresponding to the respective filter index sets in the FIR filter and extracted from the total filter coefficient sequence from the filter coefficient acquiring apparatus.
  • the processing circuitry 30 stores the partial filter coefficient sequences in the memory 33.
  • the range determination function 31 of the processing circuitry 30 determines the range of the amplification factor determined to be included in the filter index set.
  • the range determination function 31 performs the range determination described above on all the filter index sets, thereby determining the ranges of the amplification factor for the respective filter index sets having a smaller number of elements than the number of elements of the total filer coefficient sequence.
  • the representative coefficient determination function 32 determines a representative coefficient representing the filter coefficients in each of the ranges of the amplification factor.
  • the FIR filter is expressed by Expression (1).
  • the left side of Expression (1) corresponds to an output signal from the FIR filter.
  • a(n) in the right side of Expression (1) corresponds to the n-th filter coefficient
  • f(t-n) in the right side corresponds to an input signal input to the FIR filter.
  • n in Expression (1) represents the filter index.
  • the total number of filter coefficients in the FIR filter is N.
  • the range of the amplification factor is set by comparing an arithmetic result using the partial filter coefficient sequence with a threshold or comparing the partial filter coefficient sequence with a threshold, which will be described later.
  • the range determination function 31 performs the following processing as an outline of parameter determination:
  • FIG. 4 is a flowchart of an example of the process of parameter determination.
  • the range determination function 31 determines whether the n-th (0 ⁇ n ⁇ N-1) filter coefficient a(n) can be treated as a filter coefficient having the same value as the (n-1)-th filter coefficient a(n-1).
  • the range determination function 31 adds the filter index of the n-th filter coefficient a(n) to the same filter index set D m as that of the (n-1)-th filter coefficient a(n-1).
  • the range determination function 31 increments n. In other words, the range determination function 31 adds 1 to n to generate a second n.
  • the range determination function 31 performs the processing at Step S302 and subsequent steps again. If the second n exceeds N, the range determination function 31 performs processing at Step S307. As a result, the partial filter coefficient sequences are extracted from the total filter coefficient sequence.
  • the range determination function 31 may perform the determination at Step S302 as follows: if a(n 0 (m) )-Threshold ⁇ a(n) ⁇ a(n 0 (m) )+Threshold is satisfied where n 0 (m) is the filter index added to the filter index set D m first, and n is the filter index to be determined at Step S302, for example, the range determination function 31 may determine that the n-th filter coefficient a(n) can be treated as a filter coefficient having the same value.
  • the range determination function 31 may perform the determination at Step S302 by another method of computing in advance the sum of filter coefficients belonging to the m-th filter index set D m for the n-1-th filter coefficient, for example.
  • denotes the total number of elements in the filter index set D m (hereinafter, referred to as a duration length).
  • the range determination function 31 may determine the absolute value of the difference between the product of the n-th filter coefficient a(n) and the duration length
  • the range determination function 31 determines whether the absolute value of the difference exceeds the threshold and performs one of the processing at Step S303 (when the absolute value does not exceed the threshold) and the processing at Step S304 (when the absolute value exceeds the threshold).
  • the range determination function 31 may perform the determination at Step S302 by still another method of adding the n-th filter coefficient a(n) to the sum of filter coefficients belonging to the filter index set D m for the n-1-th filter coefficient and computing the average value of the filter coefficients, for example.
  • the range determination function 31 determines the maximum value and the minimum value of the filter coefficients in the filter coefficients belonging to the filter index set D m and the n-th filter coefficient a(n).
  • the range determination function 31 determines whether the product of the value obtained by subtracting the average value from the maximum value multiplied by (
  • FIG. 5 is a diagram of an example of the representative coefficients computed from the partial filter coefficient sequences for the respective filter index sets. As illustrated in FIG. 5 and expressed by Expression (2), the representative coefficient is computed as the average of the filter coefficients in the partial filter coefficient sequence.
  • the processing circuitry 30 outputs the representative coefficient and the filter index set relating to the range of the amplification factor to the signal processing apparatus 1.
  • the memory 13 of the signal processing apparatus 1 stores therein the representative coefficient and the filter index set in a manner associated with the range of the amplification factor.
  • Filter parameters determined by the range determination function 31 and the representative coefficient determination function 32 correspond to the representative coefficient and the filter index set relating to the range of the amplification factor. If the filter coefficient is a complex number, the representative coefficient is also a complex number.
  • the FIR filter can be performed if the complex number is an object to be processed.
  • FIG. 6 is a diagram of an example of a graph indicating an output signal (dotted line) in actual measurement and an output signal (dotted line) based on the representative coefficient and an example of the representative coefficient c m and the duration length
  • the output signal represented by the dotted line is approximated by the representative coefficient c m , which is a piecewise constant.
  • Step S308 determination of the film parameters is completed. Setting the range of the amplification factor and computing the representative coefficient in the parameter determination corresponds to approximate run-length encoding allowing an error by a threshold for the filter index set, for example.
  • the signal processing apparatus 1 includes the communication interface 11, the memory 13, and the processing circuitry 15.
  • the communication interface 11 acquires the filter parameters determined by the filter parameter determining apparatus 3 from the filter parameter determining apparatus 3 via the network.
  • the communication interface 11 acquires an input signal f(t) from the signal input apparatus 7 via the network.
  • the acquired filter parameters and the input signal f(t) are stored in the memory 13.
  • the communication interface 11 outputs output results of the FIR filter computed by the processing circuitry 15 that has received the input signal f(t) to the result output apparatus 5 via the network.
  • the memory 13 is provided as storage circuitry that stores therein various kinds of information.
  • the embodied aspect of the memory 13 is not explained herein because it is the same as the memory described in the explanation of the filter parameter determining apparatus 3.
  • the memory 13 stores therein various kinds of data received via the communication interface 11.
  • the received various kinds of data include the filter parameters and the input signal f(t), for example.
  • the memory 13 may store therein an output result g(t) output from the processing circuitry 15.
  • the output result is the output signal g(t) obtained by performing the FIR filter on the input signal f(t) and outputting it from the FIR filter.
  • the processing circuitry 15 is provided as a processor and includes an integral value computation function 151 and a signal value computation function 153, for example.
  • the processing circuitry 15 that implements the integral value computation function 151 and the signal value computation function 153 corresponds to an integral value computing unit and a signal value computing unit.
  • Various functions, such as the integral value computation function 151 and the signal value computation function 153, are stored in the memory 13 as computer-executable programs.
  • the processing circuitry 15, for example, reads out a computer program from the memory 13 and executes it, thereby implementing the function corresponding to the computer program.
  • the processing circuitry 15 that has read out the computer programs has various functions, such as the integral value computation function 151 and the signal value computation function 153.
  • the embodied aspect of the processor is not explained herein because it is the same as the processor described in the explanation of the filter parameter determining apparatus 3.
  • the integral value computation function 151 of the processing circuitry 15 adds a value not overlapping a first input sequence in a second input sequence to a first integral value and subtracts a value not overlapping the second input sequence in the first input sequence from the first integral value for each of a plurality of elements in a coefficient sequence, thereby computing a second integral value.
  • the integral value computation function 151 adds up a first input signal throughout the filter indices in the filter index set for each of the ranges of the amplification factor for the first input signal input to the finite impulse response digital filter, thereby computing the first integral value.
  • the signal value computation function 153 of the processing circuitry 15 adds up the product of the representative coefficient and the first integral value computed for each of the ranges of the amplification factor throughout the set indices, thereby computing a first output signal value to be output from the FIR filter.
  • the integral value computation function 151 After receiving a second input signal next to the first input signal, the integral value computation function 151 adds the second input signal shifted by the maximum filter index to the first integral value and subtracts the first input signal shifted by the minimum filter index from the first integral value for each of the ranges of the amplification factor, thereby computing the second integral value.
  • the signal value computation function 153 adds up the product of the representative coefficient and the second integral value throughout the set indices, thereby computing a second output signal value to be output from the FIR filter.
  • the integral value computation function 151 After receiving a third input signal next to the second input signal, the integral value computation function 151 adds the third input signal shifted by the maximum filter index to the second integral value and subtracts the second input signal shifted by the minimum filter index from the second integral value for each of the ranges of the amplification factor, thereby computing a third integral value.
  • the signal value computation function 153 adds up the product of the representative coefficient and the third integral value throughout the set indices, thereby computing a third output signal value to be output from the FIR filter.
  • the number of bits in the filter coefficients, the representative coefficients, the first integral values, and the second integral values according to the present embodiment is larger than the number of bits of the input signal f(t).
  • the number of bits in the filter coefficients, the representative coefficients, the first integral values, and the second integral values is larger than 32, for example.
  • FIG. 7 is a flowchart of an example of the process of filter arithmetic processing.
  • the integral value computation function 151 stores the computed first integral value I m (t) in the memory 13.
  • the memory 13 stores therein a plurality of first integral values corresponding to the set index m.
  • the signal value computation function 153 stores the computed first output signal value in the memory 13.
  • the integral value computation function 151 responds to input of the next input signal (hereinafter, referred to as a second input signal) and computes a second integral value I m (t+1) for each set index m based on the filter index set D m , the first integral value I m (t), the first input signal, and the second input signal. Input of the second input signal corresponds to incrementing the argument t of the input signal f(t) (t ⁇ t+1).
  • FIG. 8 is a diagram of an example of an interval in which the first integral value is computed (hereinafter, referred to as a prior integration interval) PID.
  • the horizontal axis in FIG. 8 is the pseudo time-axis direction, for example.
  • an integral value in an integration interval by the second input signal (hereinafter, referred to as a next integration interval) is computed using the first integral value in the prior integration interval PID.
  • FIG. 9 is a diagram of an example of the prior integration interval PID, a next integration interval NID, and an outline of computation performed on the next integration interval NID.
  • the integral value computation function 151 computes the second integral value I m (t+1) in the next integration interval NID for each set index m using the first integral value corresponding to the prior integration interval PID.
  • the integral value computation function 151 adds a second input signal f(t+1-max D m ) shifted by the maximum filter index (max D m ) to the first integral value I m (t) and subtracts a first input signal f(t-min D m ) shifted by the minimum filter index (min D m ) from the first integral value I m (t) for each of the ranges of the amplification factor, that is, for each set index m, thereby computing the second integral value I m (t+1).
  • the integral value computation function 151 computes the second integral value I m (t+1) based on Expression (6).
  • I m t + 1 I m t + f t + 1 ⁇ max D m ⁇ f t ⁇ min D m
  • the first term I m (t) of the right side in Expression (6) represents the first integral value.
  • the second term f(t+1-max D m ) of the right side in Expression (6) represents the second input signal in an interval Ad in FIG. 9 .
  • the third term f(t-min D m ) of the right side in Expression (6) represents the first input signal in an interval Sub in FIG. 9 .
  • the second integral value I m (t+1) in the next integration interval NID is computed by subtracting the first input signal corresponding to the interval Sub from the first integral value I m (t) corresponding to the prior integration interval PID and adding the second input signal corresponding to the interval Ad to the first integral value I m (t).
  • the integral value computation function 151 computes the second integral value I m (t+1) in the next integration interval NID.
  • the second integral value I m (t+1) is computed at this step using a one-dimensional variant of a sliding window algorithm similar to the Viola-Jones algorithm, for example.
  • the first integral value I m (t) has already been computed as expressed by Expression (6).
  • NID the number of times of computation of the second integral value I m (t+1) in the next integration interval NID is significantly reduced.
  • the order of computational complexity is reduced from N corresponding to Expression (1) to the duration length
  • the signal value computation function 153 stores the computed second output signal value in the memory 13.
  • Step S606 Receiving the next input signal corresponds to incrementing the argument of the output signal. If the next input signal is not received, the processing at Step S607 is performed.
  • the integral value computation function 151 computes a third integral value for each set index m of the filter index set based on the filter index set D m , the second integral value I m (t+1), and the third input signal next to the second input signal.
  • the integral value computation function 151 updates the second integral value with the third integral value computed at this step and stores it in the memory 13. Computation of the third integral value at this step is not explained herein because it is the same as the computation at Step S603. After this step, the processing at Step S604 is performed.
  • the signal processing apparatus 1 transmits the second signal output value output from the FIR filter to the result output apparatus 5 via the communication interface 11. After this step, the filter arithmetic processing is completed.
  • the signal processing apparatus 1 adds up the first input signal throughout the filter indices for each of the ranges of the amplification factor, thereby computing the first integral value.
  • the signal processing apparatus 1 adds up the product of the representative coefficient and the first integral value computed for each of the ranges of the amplification factor throughout the set indices, thereby computing the first output signal value.
  • the signal processing apparatus 1 After receiving the second input signal next to the first input signal, the signal processing apparatus 1 adds the second input signal shifted by the maximum filter index to the first integral value and subtracts the first input signal shifted by the minimum filter index from the first integral value for each of the ranges of the amplification factor, thereby computing the second integral value.
  • the signal processing apparatus 1 adds up the product of the representative coefficient and the second integral value throughout the set indices, thereby computing the second output signal value to be output from the digital filter.
  • the signal processing apparatus 1 computes a plurality of first integral values (e.g., I m (t)) corresponding to a plurality of elements (e.g., a plurality of representative coefficients c m ) in a coefficient sequence (e.g., a set of c m corresponding to the sequence of the representative coefficients c m ) based on a first input sequence (e.g., f(t)).
  • the signal processing apparatus 1 computes a plurality of second integral values (e.g., I m (t+1)) corresponding to the elements based on a second input sequence (e.g., f(t+1)) next to the first input sequence.
  • the signal processing apparatus 1 includes an integral value computing unit and a signal value computing unit.
  • the integral value computing unit adds a value (e.g., f(t+1-max D m )) not overlapping the first input sequence in the second input sequence to the first integral value and subtracts a value (e.g., f(t-min D m )) not overlapping the second input sequence in the first input sequence from the first integral value for each of the elements, thereby computing the second integral value.
  • the signal value computing unit computes an output signal value (e.g., g(t+1)) corresponding to the second input sequence based on the second integral value and the coefficient sequence.
  • the coefficient sequence corresponds to a sequence of a series of amplification factors corresponding to the input sequence.
  • the elements are each a representative coefficient representing a plurality of filter coefficients included in the range defined by the values of the filter coefficients.
  • the coefficient sequence is a series of representative coefficients.
  • the signal processing apparatus 1 After receiving the third input signal next to the second input signal, the signal processing apparatus 1 according to the embodiment adds the third input signal shifted by the maximum filter index to the second integral value and subtracts the second input signal shifted by the minimum filter index from the second integral value for each of the ranges of the amplification factor, thereby computing the third integral value.
  • the signal processing apparatus 1 adds up the product of the representative coefficient and the third integral value throughout the set indices, thereby computing the third output signal value to be output from the digital filter. Shifting the input signal corresponds to delay in the input signal, for example.
  • the signal processing apparatus 1 computes a plurality of third integral values (e.g., I m (t+2)) corresponding to a plurality of elements for a third input sequence (e.g., f(t+2)) next to the second input sequence.
  • the integral value computing unit adds a value (e.g., f(t+2-max D m )) not overlapping the second input sequence in the third input sequence to the second integral value and subtracts a value (e.g., f(t+1-min D m )) not overlapping the third input sequence in the second input sequence from the second integral value for each of the elements, thereby computing the third integral value.
  • the signal value computing unit computes an output signal value (e.g., g(t+2)) corresponding to the third input sequence based on the third integral value and the coefficient sequence. Non-overlapping corresponds to delay in the input sequence, for example.
  • the representative coefficient corresponds to the average of a plurality of filter coefficients included in a plurality of ranges of the amplification factor.
  • the ranges of the amplification factor are each set by comparing an arithmetic result using a plurality of filter coefficients with a threshold or comparing the filter coefficients with a threshold.
  • the signal processing apparatus 1 performs computation of the FIR filter based on the properties of the filter coefficients in the FIR filter that the values of filter coefficients having close filter indices are often close.
  • the signal processing apparatus 1 performs the computation using the representative coefficient of the filter coefficients computed for each of the ranges of the amplification factor defined by the threshold and the second integral value computed using the one-dimensional sliding window algorithm for each of the ranges of the amplification factor.
  • the signal processing apparatus 1 performs the computation, thereby reducing computational complexity for computing the second-or-higher integral values in the FIR filter, that is, achieving acceptable computational complexity. Consequently, the signal processing apparatus 1 can output the arithmetic result of the FIR filter in an acceptable computational time.
  • the number of bits in the filter coefficients, the representative coefficients, the first integral values, and the second integral values is larger than the number of bits of the input signal.
  • the number of bits in the filter coefficients, the representative coefficients, the first integral values, and the second integral values is larger than 32, for example.
  • the filter parameter determining apparatus 3 stores therein the partial filter coefficient sequences for the respective set indices. Based on the partial filter coefficient sequence corresponding to the filter index set being processed, the filter parameter determining apparatus 3 determines the range of the amplification factor of the filter coefficients determined to be included in the filter index set. The filter parameter determining apparatus 3 determines the representative coefficient representing the filter coefficients for each of the ranges of the amplification factor. As a result, the signal processing apparatus 1 can perform an arithmetic operation of the FIR filter using the representative coefficient. Consequently, the signal processing apparatus 1 can output the arithmetic result of the FIR filter in an acceptable computational time.
  • the signal processing apparatus 1 can increase the computational speed of the FIR filter and put an FIR filter with a large number of taps to practical use.
  • the present embodiment is an application example of filter arithmetic processing relating to a magnetic resonance imaging apparatus including the signal processing apparatus 1.
  • the signal processing apparatus 1 mounted on the magnetic resonance imaging apparatus uses a signal value (hereinafter, referred to as a control signal value) for controlling an electric current to be supplied to a gradient coil in a pulse sequence as the input signal described above and outputs a position in a k-space (hereinafter, referred to as a k-space position) as the second output signal value.
  • the magnetic resonance imaging apparatus corrects the k-space position of magnetic resonance data (hereinafter, referred to as MR data) generated by performing the pulse sequence or modifies the pulse sequence based on the k-space position.
  • Outputting the second output signal value according to the present embodiment is effective especially for a smooth waveform in impulse response relating to a gradient coil.
  • an electric current waveform for generating the gradient magnetic field may be deformed from an expected waveform based on the pulse sequence. Because of this, the k-space position output from the signal processing apparatus 1 as the second output signal value may represent an offset position with respect to an expected position in the k-space based on the pulse sequence (hereinafter, referred to as an ideal position).
  • FIG. 10 is a block diagram of an example of a magnetic resonance imaging apparatus 100 according to the present embodiment. As illustrated in FIG. 10 , the magnetic resonance imaging apparatus 100 includes the signal processing apparatus 1. In a modification of the present embodiment, the integral value computation function 151 and the signal value computation function 153 may be mounted on processing circuitry 125.
  • the magnetic resonance imaging apparatus 100 includes a static magnetic field magnet 101, a gradient coil 103, a gradient magnetic field power source 105, a couch 107, couch control circuitry 109, transmission circuitry 113, a transmission coil 115, a reception coil 117, reception circuitry 119, imaging control circuitry 121, a storage apparatus 123, processing circuitry 125, an input/output interface 127, and the signal processing apparatus 1.
  • the static magnetic field magnet 101 is a hollow magnet having a substantially tubular shape.
  • the static magnetic field magnet 101 generates a substantially uniform static magnetic field in the internal space.
  • the static magnetic field magnet 101 is a superconducting magnet, for example.
  • the gradient coil 103 is a hollow coil having a substantially tubular shape and is disposed on the inner surface of a tubular cooling container.
  • the gradient coil 103 independently receives an electric current from the gradient magnetic field power source 105 and generates a gradient magnetic field having magnetic field intensity that changes along X-, Y-, and Z-axes orthogonal to one another.
  • the gradient magnetic field in the X-, Y-, and Z-axes generated by the gradient coil 103 forms a slice selection gradient magnetic field, a phase encoding gradient magnetic field, and a frequency encoding gradient magnetic field (hereinafter, referred to as a read-out gradient magnetic field), for example.
  • the slice selection gradient magnetic field is used to optionally determine an imaging section.
  • the phase encoding gradient magnetic field is used to change the phase of magnetic resonance signals depending on the spatial position.
  • the frequency encoding gradient magnetic field is used to change the frequency of magnetic resonance signals depending on the spatial position.
  • the gradient magnetic field power source 105 is a power supply apparatus that supplies an electric current to the gradient coil 103 under the control of the imaging control circuitry 121.
  • the couch 107 is an apparatus including a couchtop 1071 on which a subject P is placed.
  • the couch 107 inserts the couchtop 1071 with the subject P placed thereon into a bore 111 under the control of the couch control circuitry 109.
  • the couch control circuitry 109 controls the couch 107.
  • the couch control circuitry 109 drives the couch 107 based on an instruction given by an operator through an input/output interface 17, thereby moving the couchtop 1071 in the longitudinal and vertical directions and the horizontal direction in some cases.
  • the transmission circuitry 113 supplies high frequency pulses modulated at the Larmor frequency to the transmission coil 115 under the control of the imaging control circuitry 121.
  • the transmission circuitry 113 includes an oscillating unit, a phase selecting unit, a frequency converting unit, an amplitude modulating unit, and an RF amplifier, for example.
  • the oscillating unit generates RF pulses at a resonance frequency unique to a target atomic nucleus in the static magnetic field.
  • the phase selecting unit selects the phase of the RF pulses generated by the oscillating unit.
  • the frequency converting unit converts the frequency of the RF pulses output from the phase selecting unit.
  • the amplitude modulating unit modulates the amplitude of the RF pulses output from the frequency converting unit based on the sinc function, for example.
  • the RF amplifier amplifies the RF pulses output from the amplitude modulating unit and supplies them to the transmission coil 115.
  • the transmission coil 115 is a radio frequency (RF) coil disposed on the inner side of the gradient coil 103.
  • the transmission coil 115 generates RF pulses corresponding to a high frequency magnetic field due to output from the transmission circuitry 113.
  • the reception coil 117 is an RF coil disposed on the inner side of the gradient coil 103.
  • the reception coil 117 receives magnetic resonance signals output from the subject P by the high frequency magnetic field.
  • the reception coil 117 outputs the received magnetic resonance signals to the reception circuitry 119.
  • the transmission coil 115 and the reception coil 117 may be provided as an integrated transmission/reception coil.
  • the reception circuitry 119 generates MR data corresponding to digital MR signals (hereinafter, referred to as MR data) based on the magnetic resonance signals output from the reception coil 117 under the control of the imaging control circuitry 121. Specifically, the reception circuitry 119 performs various signal processing on the MR signals output from the reception coil 117. Subsequently, the reception circuitry 119 performs analog to digital (A/D) conversion (hereinafter, referred to as A/D conversion) on the data resulting from the various signal processing to generate MR data. The reception circuitry 119 outputs the generated MR data to the imaging control circuitry 121.
  • A/D analog to digital
  • the imaging control circuitry 121 controls the gradient magnetic field power source 105, the transmission circuitry 113, the reception circuitry 119, and other components according to an imaging protocol output from the processing circuitry 125 and performs imaging on the subject P.
  • the imaging protocol has a pulse sequence corresponding to the type of an examination.
  • the imaging protocol defines imaging parameters such as the magnitude of an electric current supplied to the gradient coil 103 by the gradient magnetic field power source 105, the timing at which the gradient magnetic field power source 105 supplies the electric current to the gradient coil 103, the magnitude and the time width of high frequency pulses supplied to the transmission coil 115 by the transmission circuitry 113, the timing at which the transmission coil 115 supplies the high frequency pulses to the transmission coil 115, and the timing at which the reception coil 117 receives MR signals, for example.
  • the imaging control circuitry 121 When the imaging control circuitry 121 images the subject P by driving the gradient magnetic field power source 105, the transmission circuitry 113, the reception circuitry 119, and other components and receives MR data from the reception circuitry 119, it transfers the received MR data to the processing circuitry 125 or the like.
  • the imaging control circuitry 121 is provided as a processor, for example.
  • the storage apparatus 123 stores therein various computer programs executed by the processing circuitry 125, various imaging protocols, and imaging conditions including a plurality of imaging parameters defining the imaging protocols, for example.
  • the embodied aspect of the storage apparatus 123 is not explained herein because it is the same as the memory 13.
  • Data stored in the storage apparatus 123 may be stored in the memory 13. In this case, the memory 13 functions as an alternative to the storage apparatus 123.
  • the processing circuitry 125 includes a system control function 131, a reconstruction function 133, and an adjustment function 135. Various functions implemented by the system control function 131, the reconstruction function 133, and the adjustment function 135 are stored in the storage apparatus 123 as computer-executable programs.
  • the processing circuitry 125 is a processor that reads out the computer programs corresponding to the various functions from the storage apparatus 123 and executes the read-out programs, thereby implementing the functions corresponding to the respective computer programs. In other words, the processing circuitry 125 that has read out the computer programs has a plurality of functions or the like illustrated in the processing circuitry 125 in FIG. 10 .
  • the processing circuitry 125 that implements the system control function 131, the reconstruction function 133, and the adjustment function 135 corresponds to a system controlling unit, a reconstructing unit, and an adjusting unit. Hardware resources that fabricate the processing circuitry 125 are not explained herein because they are the same as those of the processing circuitry 125 mounted on the signal processing apparatus 1.
  • the system control function 131 reads out a system control program stored in the storage apparatus 123 and loads it on a memory.
  • the system control function 131 controls the circuitry of the magnetic resonance imaging apparatus 100 according to the loaded system control program.
  • the system control function 131 for example, reads out an imaging protocol from the storage apparatus 123 based on imaging conditions input by the operator through the input/output interface 127.
  • the system control function 131 may generate the imaging protocol based on the imaging conditions.
  • the system control function 131 transmits the imaging protocol to the imaging control circuitry 121 and controls imaging on the subject P.
  • the reconstruction function 133 fills the k-space with MR data along a read-out direction in the k-space based on the strength of the read-out gradient magnetic field.
  • the reconstruction function 133 performs inverse Fourier transform on the MR data with which the k-space is filled, thereby generating an MR image.
  • the reconstruction function 133 outputs the MR image to the storage apparatus 123 and the input/output interface 127.
  • the adjustment function 135 corrects the position in the k-space of the MR data generated by performing the pulse sequence based on the k-space position output from the signal processing apparatus 1.
  • the k-space position output from the signal processing apparatus 1 corresponds to the strength of the read-out gradient magnetic field.
  • the adjustment function 135 corrects the position of the MR data with which the k-space is filled along the read-out direction in the k-space based on the k-space position.
  • the reconstruction function 133 performs inverse Fourier transform on the MR data with which the k-space is filled using the corrected position in the k-space, thereby generating an MR image.
  • correcting the position in the k-space refers to, for example, correcting, to an ideal position, the position in the k-space of the MR data generated by performing the pulse sequence based on the k-space position output from the signal processing apparatus 1. More specifically, correcting the position in the k-space refers to obtaining, as an amount of shift, an offset between the ideal position and the k-space position output from the signal processing apparatus 1 to shift the position in the k-space of the MR data generated by performing the pulse sequence to the ideal position.
  • the adjustment function 135 modifies the pulse sequence relating to main scanning based on the k-space position output from the signal processing apparatus 1. Specifically, the adjustment function 135 modifies an electric current waveform for generating the gradient magnetic field, such as the magnitude of an electric current to be supplied to the gradient coil 103 and the timing at which the gradient magnetic field power source 105 supplies the electric current to the gradient coil 103, and the timing at which the reception coil 117 receives MR signals, for example, in the pulse sequence based on the k-space position.
  • the timing is a timing at which an A/D converter in the reception circuitry 119 is turned ON, for example.
  • the imaging control circuitry 121 performs scanning on the subject P using the modified pulse sequence.
  • Modifying the pulse sequence relating to main scanning refers to, for example, obtaining, as an amount of shift, an offset between the ideal position and the k-space position output from the signal processing apparatus 1, and correcting either or both of the strength of the gradient magnetic field and a length of time for which the gradient magnetic field is applied so that the position in the k-space of the MR data generated by performing the pulse sequence is shifted to the ideal position. More specifically, modifying the pulse sequence refers to correcting either or both of a signal value to be supplied to the gradient coil 103 and a length of time for which the signal value is applied. The signal value corresponds to a voltage to be supplied to the gradient coil 103.
  • the processing corresponds to performing the FIR filter using the filter parameter and the control signal value and performing the correction or the modification described above using the k-space position corresponding to the output result from the FIR filter (hereinafter, referred to as adjustment).
  • the input/output interface 127 includes an input interface and an output interface.
  • the input interface includes circuitry relating to a pointing device, such as a mouse, or an input device, such as a keyboard, and an input terminal from the network, for example.
  • the circuitry included in the input interface is not limited to circuitry relating to physical operating parts, such as a mouse and a keyboard.
  • the input interface may include processing circuitry that receives electrical signals corresponding to an input operation from an external input device provided separately from the magnetic resonance imaging apparatus 100 and outputs the received electrical signals to various kinds of circuitry.
  • the output interface includes a display and an output terminal to the network, for example.
  • the display displays various MR images reconstructed by the reconstruction function 133 and various kinds of information on imaging and image processing under the control of the processing circuitry 131.
  • the display is a display device, such as a CRT display, a liquid crystal display, an organic EL display, an LED display, a plasma display, or other desired displays or monitors known in the present technical field.
  • FIG. 11 is a flowchart of an example of the process of adjustment.
  • the processing contents from Step S101 to Step S106 in the flowchart in FIG. 11 corresponds to the processing contents from Step S601 to Step S606 in the flowchart in FIG. 7 approximately changed so as to match the magnetic resonance imaging apparatus 100.
  • the following describes part of the processing contents in the flowchart in FIG. 11 different from those in the flowchart in FIG. 7 .
  • the integral value computation function 151 responds to reception of a first control signal value serving as the first input signal f(t) and computes a first integral value for each of the ranges of the amplification factor, that is, for each set index using the first control signal value. Specifically, the integral value computation function 151 computes the first integral value I m (t) for each set index m using the elements of the filter index set D m and the first control signal value f(t) based on Expression (4). The integral value computation function 151 stores the computed first integral value I m (t) in the memory 13 in a manner associated with the set index m.
  • the signal value computation function 153 computes a first output signal value as a first k-space position using a plurality of representative coefficients c m and a plurality of first integral values I m (t). Specifically, the signal value computation function 153 adds the first integral value I m (t) throughout the set indices m, thereby computing a first k-space position g(t) using Expression (5). The signal value computation function 153 stores the computed first k-space position in the memory 13.
  • the integral value computation function 151 responds to input of the next input signal (hereinafter, referred to as a second control signal value) and computes a second integral value I m (t+1) for each set index m based on the filter index set D m , the first integral value I m (t), the first control signal value, and the second control signal value. Specifically, the integral value computation function 151 adds a second control signal value f(t+1-max D m ) to the first integral value I m (t) and subtracts a first input signal f(t-min D m ) from the first integral value I m (t), thereby computing the second integral value I m (t+1) as expressed by Expression (6).
  • the signal value computation function 153 computes a second output signal value as a second k-space position using a plurality of representative coefficients c m and a plurality of second integral values I m (t+1). Specifically, the signal value computation function 153 adds up the second integral value I m (t+1) throughout the set indices m, thereby computing a second k-space position g(t+1) as expressed by Expression (7). The signal value computation function 153 stores the computed second k-space position in the memory 13.
  • the processing at Step S106 is performed. If the next control signal value is not received, the processing at Step S107 is performed.
  • the second k-space position is output to the processing circuitry 125 of the magnetic resonance imaging apparatus 100.
  • the integral value computation function 151 computes an integral value for each set index m based on the filter index set D m , the second integral value I m (t+1), and the control signal value next to the second control signal value.
  • the integral value computation function 151 updates the second integral value with the integral value computed at this step and stores it in the memory 13. Computation of the integral value at this step is not explained herein because it is the same as the computation at Step S103. After this step, the processing at Step S104 is performed.
  • the adjustment function 135 corrects the position in the k-space in the MR data or modifies the pulse sequence based on the second k-space position. After the main scanning is performed on the subject P, for example, the adjustment function 135 corrects the k-space position in the MR data based on the second k-space position. At this time, the reconstruction function 133 fills the k-space with the MR data using the corrected k-space position. Subsequently, the reconstruction function 133 performs inverse Fourier transform on the MR data with which the k-space is filled, thereby generating an MR image.
  • the adjustment function 135 may modify the pulse sequence based on the second k-space position. Before the main scanning is performed on the subject P, for example, the adjustment function 135 modifies the imaging parameter in the pulse sequence relating to the main scanning based on the second k-space position. Subsequently, the imaging control circuitry 121 performs the main scanning on the subject P using the modified pulse sequence.
  • the signal processing apparatus 1 outputs the position in the k-space as the second output signal value using the signal value for controlling an electric current to be supplied to the gradient coil 103 in the pulse sequence as the input signal.
  • the magnetic resonance imaging apparatus 100 corrects the position in the k-space of magnetic resonance data generated by performing the pulse sequence or modifies the pulse sequence based on the position in the k-space.
  • the magnetic resonance imaging apparatus 100 can output an accurate gradient magnetic field strength as the second output signal value.
  • the signal processing apparatus 1 outputs the position in the k-space as the output signal value using the signal value for controlling an electric current to be supplied to the gradient coil 103 in the pulse sequence as the input sequence.
  • the magnetic resonance imaging apparatus 100 corrects the position in the k-space of the magnetic resonance data generated by performing the pulse sequence or modifies the pulse sequence based on the position in the k-space.
  • the magnetic resonance imaging apparatus 100 can perform a convolution operation required for computing the gradient magnetic field strength on a significantly long impulse response associated with an eddy current using the control signal value as an impulse in an acceptable short time.
  • the magnetic resonance imaging apparatus 100 can perform a convolution operation on a significantly long impulse response used by an emphasis filter for cancelling such an eddy current in an acceptable short time, for example.
  • the signal processing apparatus 1 in the magnetic resonance imaging apparatus 100 can compute the second output signal value with high accuracy and high resolution in a short time without using the Bloch simulator deviating from reality or infinite impulse response (hereinafter, referred to as IIR) by a model assumed by exponential impulse response.
  • IIR infinite impulse response
  • the magnetic resonance imaging apparatus 100 can fabricate the FIR filter that can accurately compute impulse response (deformation of the waveform of the gradient magnetic field strength) relating to an eddy current in an electric current to be supplied to the gradient coil 103 at high speed and that matches the physical state or the logical design in a simpler manner.
  • the magnetic resonance imaging apparatus 100 modifies the pulse sequence based on the design value in a manner matching an actual physical state in the gradient coil 103 or corrects the k-space position of the MR data in a manner matching an actual k-space position. Consequently, the magnetic resonance imaging apparatus 100 can reduce the gap between the design value and the actual position in the k-space in a trajectory in the k-space and suppress degradation of the image quality of a generated MR image.
  • Other advantageous effects according to the present embodiment are not explained herein because they are the same as those according to the first embodiment.
  • the signal processing apparatus 1 can compute impulse response of sound by simulating various acoustic (sound reflection) models (e.g., a reflected sound source model or ray tracing).
  • the present application example may input a sequence of constants piecewise as the input sequence or output a sequence of constants piecewise.
  • the control signal value for controlling an electric current to be supplied to the gradient coil as the input sequence.
  • the control signal value is also referred to as a gradient system transfer function (GSTF).
  • FIG. 12 is a diagram of an example of a graph of the gradient system transfer function (GSTF) and an example of the sum of input signals in D m and the duration length.
  • GSTF gradient system transfer function
  • the second integral value is output piecewise as a sequence of constants. Whether to skip the addition and the subtraction is determined by the integral value computation function 151 monitoring the input sequence in real time, for example.
  • the integral value computation function 151 determines whether to skip the addition and the subtraction based on the duration length
  • the present application example can skip the addition and the subtraction in filter arithmetic processing.
  • the input sequence has 0s from 1000 to 1,000,000. For this reason, the addition and the subtraction in filter arithmetic processing are skipped. Consequently, the present application example can perform filter arithmetic processing at higher speed.
  • Other advantageous effects according to the present application example are not explained herein because they are the same as those according to the first embodiment.
  • the signal processing method includes: adding up the first input signal throughout the filter indices for distinguishing a plurality of filter coefficients belonging to a plurality of ranges of the amplification factor for the first input signal input to the FIR filter for each of the ranges of the amplification factor, thereby computing the first integral value; adding up the product of the representative coefficient representing the filter coefficients and the first integral value computed for each of the ranges of the amplification factor throughout the set indices for distinguishing sets of the filter indices, thereby computing the first output signal value to be output from the digital filter; adding, after receiving the second input signal next to the first input signal, the second input signal shifted by the maximum filter index to the first integral value and subtracting the first input signal shifted by the minimum filter index from the first integral value for each of the ranges of the amplification factor, thereby computing the second integral value; and adding up the product of the representative coefficient and the second integral value throughout the set indices, thereby computing the second integral value;
  • the signal processing method includes: computing a plurality of first integral values corresponding to a plurality of elements in the coefficient sequence based on the first input sequence; adding a value not overlapping the first input sequence in the second input sequence to the first integral value and subtracting a value not overlapping the second input sequence in the first input sequence from the first integral value for each of the elements, thereby computing the second integral value; and computing the output signal value corresponding to the second input sequence based on the second integral value and the coefficient sequence.
  • the signal processing program including instructions that cause a computer to execute: adding up the first input signal throughout the filter indices for distinguishing a plurality of filter coefficients belonging to a plurality of ranges of the amplification factor for the first input signal input to the FIR filter for each of the ranges of the amplification factor, thereby computing the first integral value; adding up the product of the representative coefficient representing the filter coefficients and the first integral value computed for each of the ranges of the amplification factor throughout the set indices for distinguishing sets of the filter indices, thereby computing the first output signal value to be output from the digital filter; adding, after receiving the second input signal next to the first input signal, the second input signal shifted by the maximum filter index to the first integral value and subtracting the first input signal shifted by the minimum filter index from the first integral value for each of the ranges of the amplification factor, thereby computing the second integral value; and adding up the product of the representative coefficient and the second integral value throughout the set
  • the signal processing program comprising instructions that cause a computer to execute: computing a plurality of first integral values corresponding to a plurality of elements in the coefficient sequence based on the first input sequence; adding a value not overlapping the first input sequence in the second input sequence to the first integral value and subtracting a value not overlapping the second input sequence in the first input sequence from the first integral value for each of the elements, thereby computing the second integral value; and computing the output signal value corresponding to the second input sequence based on the second integral value and the coefficient sequence.
  • the filter arithmetic processing can be performed by installing the signal processing program on a computer in the magnetic resonance imaging apparatus 100 or various signal processing servers and loading it on a memory, for example.
  • the computer program that can cause the computer to perform the method may be stored and distributed in a storage medium, such as a magnetic disk (e.g., a hard disk), an optical disc (e.g., a CD-ROM and a DVD), and a semiconductor memory.
  • a storage medium such as a magnetic disk (e.g., a hard disk), an optical disc (e.g., a CD-ROM and a DVD), and a semiconductor memory.
  • At least one of the embodiments and the like described above can put a finite impulse response digital filter having a great number of taps to practical use.

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